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import pandas as pd | |
import re | |
import gradio as gr | |
from sklearn.model_selection import train_test_split | |
from sklearn.pipeline import make_pipeline | |
from sklearn.feature_extraction.text import TfidfVectorizer | |
from sklearn.linear_model import LogisticRegression | |
from sklearn.metrics import accuracy_score | |
from collections import Counter | |
# 1. Load and clean data | |
df = pd.read_csv("spam.csv", encoding="latin1")[["v1", "v2"]] | |
df.columns = ["label", "text"] | |
df["label"] = df["label"].map({"ham": 0, "spam": 1}) | |
# 2. Clean text | |
def clean_text(text): | |
text = text.lower() | |
text = re.sub(r"\W+", " ", text) | |
return text.strip() | |
df["text"] = df["text"].apply(clean_text) | |
# 3. Split data | |
X_train, X_test, y_train, y_test = train_test_split( | |
df["text"], df["label"], test_size=0.2, stratify=df["label"], random_state=42 | |
) | |
# 4. Build and train model | |
model = make_pipeline( | |
TfidfVectorizer(ngram_range=(1, 2), stop_words="english"), | |
LogisticRegression(max_iter=1000, class_weight="balanced") | |
) | |
model.fit(X_train, y_train) | |
# 5. Evaluate | |
accuracy = accuracy_score(y_test, model.predict(X_test)) | |
print(f"Validation Accuracy: {accuracy:.2%}") | |
# 6. Gradio prediction function | |
def predict_spam(message): | |
cleaned = clean_text(message) | |
pred = model.predict([cleaned])[0] | |
prob = model.predict_proba([cleaned])[0][pred] | |
label = "π« Spam" if pred == 1 else "π© Not Spam (Ham)" | |
return f"{label} (Confidence: {prob:.2%})" | |
# 7. Gradio UI | |
gr.Interface( | |
fn=predict_spam, | |
inputs=gr.Textbox(lines=4, label="Enter SMS Message"), | |
outputs=gr.Text(label="Prediction"), | |
title="SMS Spam Detector", | |
description=f"Detects spam in SMS messages. Trained on uploaded CSV (Accuracy: {accuracy:.2%})." | |
).launch() | |